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Adaptive Diversity of Genetic Algorithm for QoS-based Cloud Service Selection
| Content Provider | Semantic Scholar |
|---|---|
| Author | Zhang, Chengwen Xun, Zhaoyong Kuang, Jian Cheng, Bo Zhang, Lei |
| Copyright Year | 2013 |
| Abstract | An elitist selection adaptive Genetic Algorithm i s presented to select an optimal cloud service compos ite plan from a lot of composite plans on the basis of globa l Qualityof-Service (QoS) constraints. In this Algorithm, so me strategies are provided to improve convergence and population diversity and raise fitness result. Thes e strategies include an elitist selection strategy, an individua l selection method based on ranking selection and an adaptive m utation strategy. The elitist selection strategy gives a gu rantee that the degradation of population fitness can be avoide d. The individual selection method provides a simple and e ffective way to control selective pressure. The adaptive mut ation strategy uses a special population measurement to d ecide individuals’ similarity. The value of the dynamic m utation probability changes on the basis of population dive rsity. Some simulation results on cloud service selection with global QoS constraints have shown that the elitist selection a daptive Genetic Algorithm can gain better composition servi ce plan. |
| File Format | PDF HTM / HTML |
| Alternate Webpage(s) | http://weblidi.info.unlp.edu.ar/WorldComp2013-Mirror/p2013/ICA4000.pdf |
| Alternate Webpage(s) | http://worldcomp-proceedings.com/proc/p2013/ICA4000.pdf |
| Language | English |
| Access Restriction | Open |
| Content Type | Text |
| Resource Type | Article |
